Method and Apparatus for Evaluating Performance of a Read Channel

ABSTRACT

Methods and apparatus are provided for measuring the performance of a read channel. A number of detection techniques, such as SOVA and maximum-a-posteriori (MAP) detectors, produce a bit decision and a corresponding reliability value associated with the bit decision. The reliability value associated with the bit decision may be expressed, for example, in the form of log likelihood ratios (LLRs). The reliability value can be monitored and used as a performance measure. The present invention provides a channel performance measure that generally correlates directly to the BER but can be collected in less time.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a divisional of U.S. patent application Ser. No.12/750,049, filed Mar. 30, 2010, which is a divisional of U.S. patentapplication Ser. No. 11/068,224, filed Feb. 28, 2005, each incorporatedby reference herein.

FIELD OF THE INVENTION

The present invention relates generally to evaluating the performance ofa read channel, and more particularly, to methods and apparatus forevaluating the performance of read channels that employ soft outputViterbi detection.

BACKGROUND OF THE INVENTION

A magnetic recording read channel converts an analog read channel intoan estimate of the user data recorded on a magnetic medium. Read headsand magnetic media introduce noise and other distortions into the readsignal. As the information densities in magnetic recording increase, theintersymbol interference (ISI) becomes more severe as well. In readchannel chips, a Viterbi detector is typically used to detect the readdata bits in the presence of intersymbol interference and noise.

The Soft Output Viterbi Algorithm (SOVA) is a well known technique forgenerating soft decisions inside a Viterbi detector. A soft decisionprovides a detected bit with a corresponding reliability. These softdecisions can be used by an outer detector to improve the error rateperformance of the overall system. For a more detailed discussion ofSOVA detectors, see, for example, J. Hagenauer and P. Hoeher, “ViterbiAlgorithm with Soft-decision Outputs and its Applications,” IEEE GlobalTelecommunications Conference (GLOBECOM), vol. 3, 1680-1686 (November1989).

Various parameters of a magnetic recording read channel are typicallyadjusted to improve the Bit Error Rate (BER) performance. Whilemeasuring the BER provides the most accurate measure of performance, theBER measurement is unduly time consuming. A number of techniques havebeen proposed or suggested for obtaining performance measures based onthe mean squared error or other derivatives of an error term derivedfrom decoding the bit sequence and then re-creating the ideal pattern.Although these techniques are generally fast, they are not guaranteed tocorrelate directly with the BER.

A need therefore exists for an improved method and apparatus forobtaining performance measures without measuring the BER. A further needexists for a method and apparatus for obtaining performance measures ina read channel that are directly correlated to BER but takes much lesstime to collect.

SUMMARY OF THE INVENTION

Generally, methods and apparatus are provided for measuring theperformance of a read channel. A number of detection techniques, such asSOVA and maximum-a-posteriori (MAP) detectors, produce a bit decisionand a corresponding reliability value associated with the bit decision.The present invention recognizes that the reliability value can bemonitored and used as a performance measure. The present inventionprovides a channel performance measure that generally correlatesdirectly to the BER but can be collected in less time.

According to one aspect of the present invention, the reliability valuesderived from the “soft_value” provided by a SOVA detector are monitoredas a performance measure. The reliability values can be accumulated orused to generate a histogram (or both). In one exemplary implementation,a counter is configured to count the number of occurrences of thereliability in each of a number of threshold ranges. In this manner, ahistogram of reliability values can be generated.

A more complete understanding of the present invention, as well asfurther features and advantages of the present invention, will beobtained by reference to the following detailed description anddrawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic block diagram illustrating a reliability monitorincorporating features of the present invention;

FIG. 2 illustrates a number of exemplary ranges defined by theprogrammable threshold values used by the comparator of FIG. 1; and

FIG. 3 is a schematic block diagram illustrating a portion of areliability monitor in accordance with an alternate implementation ofthe present invention.

DETAILED DESCRIPTION

As previously indicated, SOVA techniques produce both a bit decision anda corresponding reliability value associated with the bit decision. Thepresent invention recognizes that the reliability value can be monitoredand used as a performance measure. In particular, the present inventionprovides a channel performance measure that generally correlatesdirectly to the BER but can be collected in less time. According to oneaspect of the present invention, the reliability values derived from the“soft value” provided by a SOVA detector are accumulated, optionallyused to generate a histogram, and then monitored as a performancemeasure for tuning the channel and recording system optimally. Thereliability values may be provided by the SOVA detector, for example, inthe form of log likelihood ratios (LLRs). The SOVA detector could bereplaced by another soft-output detector, such as a maximum-a-posteriori(MAP) detector, as would be apparent to a person of ordinary skill.

In one exemplary implementation of the invention, a threshold device isemployed that can be programmed to a range of values covering thereliability mapping. A counter is configured to count the number ofoccurrences at each threshold setting. In this manner, a histogram ofreliability values can be generated. The shape of this histogram can beinterpreted as a relative measure of performance. Even in cases wherethere is no measurable BER, the histogram information can be used tofind optimal settings for the channel parameters by comparing theresultant histograms of different settings. The granularity of thehistogram could be increased beyond the number of thresholds plus 1(see, FIG. 2), for example, by multiple reads of the same data withdifferent threshold settings.

FIG. 1 is a schematic block diagram illustrating a reliability monitor100 incorporating features of the present invention. The reliabilitymonitor 100 may be incorporated, for example, as part of a SOVAdetector, such as those described in J. Hagenauer and P. Hoeher,“Viterbi Algorithm with Soft-decision Outputs and its Applications,”IEEE Global Telecommunications Conference (GLOBECOM), vol. 3, 1680-1686(November 1989), as modified herein to provide the features andfunctions of the present invention. As shown in FIG. 1, the exemplaryreliability monitor 100 produces soft bit decision values, labeled as“soft_values” in FIG. 1. Generally, the reliability monitor 100accumulates and histograms the generated reliability information (basedon the soft values).

For each bit decision, the reliability monitor 100 generates asoft-value that is between 0 and 1, where 0 or 1 denotes the highestprobability for the detected bit being 0 or 1, respectively, and where avalue of 0.5 denotes the lowest possible bit reliability. Thereliability value is computed by a subtractor 110 as follows:

reliability_value=|soft_value−0.5|,

where the computed reliability value signal is between 0 and 0.5 and isproportional to the bit reliability. An accumulator 140 accumulatesseveral samples of the reliability_value signal and provides theaccumulated values to a set of monitor registers 160. The accumulator140 can optionally be automatically reset each time its associatedmonitor register is read out.

A comparator 130 compares the reliability_value against a number ofprogrammable thresholds that define generally non-overlapping ranges,discussed below in conjunction with FIG. 2, such as between 0 and 0.5.In the exemplary embodiment of FIG. 1, the comparator 130 employs fourprogrammable threshold values th1, th2, th3, and th4, thereby definingfive distinct ranges, Range 1 to Range 5.

As discussed further below in conjunction with FIG. 2, a bank ofcounters 150 counts the number of occurrences of the reliability_valuein each defined range. For example, a first counter 150-1 counts thenumber of occurrences of the reliability_value in a first range,Range 1. The bank of counters 150 provide the count values to the set ofmonitor registers 160. For example, the counter values can be read outthrough a bit-serial or a byte-parallel register configuration interfaceby providing a register address to the interface.

The reliability monitor 100 provides a measure of channel performancethat is directly correlated to the Bit Error Rate (BER), but takes lesstime to collect. The channel performance measure can be used, forexample, to tune the channel optimally. In one exemplary embodiment, thereliability monitor 100 accumulates normalized LLR values provided by aSOVA detector, which may be, for example, between 0 and 1, and thecomparator 130 compares them against a number of threshold values. In analternate implementation, the reliability monitor 100 can accumulatemeasured LLR values provided by a SOVA detector, which may be, forexample, in a range between −32 to +31 or −64 to +63.

FIG. 2 illustrates a number of exemplary ranges 200 defined by theprogrammable threshold values used by the comparator 130 of FIG. 1. Asshown in FIG. 2, the comparator 130 can employ five distinct rangesdefined by the four exemplary threshold values th1, th2, th3, and th4.

For example, Counter 1 (the first counter in the bank 150 of counters)counts the number of time that the computed reliability_value is withinRange 1, i.e., to 0≦|soft_value−0.5|<th1. Counter 2 counts the number oftime that the computed reliability_value is within Range 2, i.e.,th1≦|soft_value−0.5|<th2. Counter 3 counts the number of time that thecomputed reliability_value is within Range 3, i.e.,th2≦|soft_value−0.5|<th3. Counter 4 counts the number of time that thecomputed reliability_value is within Range 4, i.e.,th3≦|soft_value−0.5|<th4. Counter 5 counts the number of time that thecomputed reliability_value is within Range 5, i.e., th4≦|soft_value−0.5|≦0.5.

FIG. 3 is a schematic block diagram illustrating an alternatereliability monitor 300 incorporating features of the present invention.As shown in FIG. 3, the reliability monitor 300 receives both a knownbit value (from a bit source 305, such as a bit-sequence generator thatproduces a known bit-sequence) and a corresponding reliability valuefrom a SOVA detector, labeled as a “soft_value” in FIG. 3, associatedwith the bit decision. The same bit-sequence has been previously writtento the hard disk and is now read and detected by the SOVA detector. Thebit-sequence generator should be synchronized with the bitstream readfrom the hard disk. The bit sequence generator could be implemented, forexample, as a Linear Feedback Shift-Register (LFSR).

In the embodiment of FIG. 3, the reliability_value signal is computed bythe metric calculator 310 as follows:

${reliability\_ value} = \frac{{{soft\_ value} + {hard\_ value} - 1}}{2}$

where the hard_value signal is the known bit value provided by thebinary Bit Source 305. It is noted that this equation returns a value of0.5 (highest reliability) if both the soft_value and the hard_value areidentical and returns a value of 0 (lowest reliability) if thesoft_value is 0 and the hard value is 1 and vice versa. The metriccalculator 310 can be described by the following exemplary truth table:

Input: hard_value Output: reliability_value 0$= \frac{1 - {soft\_ value}}{2}$ 1 $= \frac{soft\_ value}{2}$The remaining elements of FIG. 3 operate in a similar manner to thecorresponding elements of FIG. 1.

The performance measure provided by the present invention directlycorrelates with the BER, as this is the same information that theViterbi detector uses to make its final decision. In addition, theinformation can be collected and interpreted more rapidly than the BER.Even in cases where there is no measurable BER, this information canstill be used to find optimal settings for the channel parameters bycomparing the resultant histograms of different settings.

It is to be understood that the embodiments and variations shown anddescribed herein are merely illustrative of the principles of thisinvention and that various modifications may be implemented by thoseskilled in the art without departing from the scope and spirit of theinvention.

1. A system for measuring performance in a read channel, comprising: amemory; and at least one processor, coupled to the memory, operative to:obtain one or more soft bit decisions for a bit read on said readchannel, wherein each of said soft bit decisions is a decision on avalue of a bit; determine a reliability value based on said one or moresoft bit decisions, wherein said reliability value is a measure ofprobability for said bit read on said read channel; determine aperformance of said read channel based on a number of occurrences ofsaid reliability value in a plurality of ranges; and provide said numberof occurrences to tune a channel and thereby improve said performance ofsaid read channel.
 2. The system of claim 1, wherein said one or moresoft bit decisions are obtained from a soft output Viterbi detector or amaximum-a-posteriori (MAP) detector.
 3. The system of claim 1, wherein asoft value associated with said one or more soft bit decisions is anormalized value between 0 and 1 indicating a probability for a detectedbit having a given binary value of 0 or 1, and where a value of 0.5denotes a lowest possible bit reliability, wherein said reliabilityvalue is computed as follows:reliability_value=|soft_value−0.5|.
 4. The system of claim 1, whereinsaid processor is further configured to obtain a hard bit decision,hard_value, and a soft value associated with said one or more soft bitdecisions, and wherein said reliability value is computed as follows:${reliability\_ value} = {\frac{{{soft\_ value} + {hard\_ value} - 1}}{2}.}$5. The system of claim 4, wherein said hard bit decision is obtainedfrom a bit-sequence generator.
 6. The system of claim 1, wherein saidnumber of occurrences is determined by comparing said reliability valueto a number of threshold values and counting said number of occurrencessaid reliability value is in each range defined by said thresholds. 7.The system of claim 6, wherein said thresholds are programmable.
 8. Thesystem of claim 6, wherein said processor is further configured togenerate one or more histograms based on one or more of said countednumber of occurrences.
 9. The system of claim 8, wherein said processoris further configured to increase a granularity of said one or morehistograms by performing multiple reads of the same data with differentthreshold settings.
 10. The system of claim 1, wherein said processor isfurther configured to accumulate several samples of said reliabilityvalue.